ctrltokyo/mistral-finetune-gaban-samsay

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Model Description

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Uses

Direct Use

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,  # Mistral, same as before
    quantization_config=bnb_config,  # Same quantization config as before
    device_map="auto",
    trust_remote_code=True,
    use_auth_token=True
)

eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

ft_model = PeftModel.from_pretrained(base_model, "ctrltokyo/mistral-finetune-gaban-samsay")

# Inference

eval_prompt = """The following is a script for an episode of Kitchen Nightmares:
[Gordon] Goddamn it, this restaurant is in the toilet!
"""
model_input = eval_tokenizer(eval_prompt, return_tensors="pt").to("cuda")

ft_model.eval()
with torch.no_grad():
    print(eval_tokenizer.decode(ft_model.generate(**model_input, max_new_tokens=150, repetition_penalty=1.5)[0], skip_special_tokens=True))

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.7.2.dev0